[1]刘雯雯,汪皖燕,程树林.融合项目热门惩罚因子改进协同过滤推荐方法[J].计算机技术与发展,2023,33(03):15-19.[doi:10. 3969 / j. issn. 1673-629X. 2023. 03. 003]
　LIU Wen-wen,WANG Wan-yan,CHENG Shu-lin.Improved Collaborative Filtering Recommendation Method Integrating Item Popularity Punishment Factor[J].,2023,33(03):15-19.[doi:10. 3969 / j. issn. 1673-629X. 2023. 03. 003]
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融合项目热门惩罚因子改进协同过滤推荐方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
33
期数:
2023年03期
页码:
15-19
栏目:
大数据与云计算
出版日期:
2023-03-10

文章信息/Info

Title:
Improved Collaborative Filtering Recommendation Method Integrating Item Popularity Punishment Factor
文章编号:
1673-629X(2023)03-0015-05
作者:
刘雯雯; 汪皖燕; 程树林
安庆师范大学 计算机与信息学院,安徽 安庆 246133
Author(s):
LIU Wen-wen; WANG Wan-yan; CHENG Shu-lin
rsity,Anqing 246133,China
关键词:
推荐系统; 热门度偏差; 协同过滤; 二分类; 评分预测
Keywords:
recommendation system; popularity bias; collaborative filtering; binary classification; rating prediction0
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2023. 03. 003
摘要:
推荐系统是大数据时代解决信息过载问题的一种重要工具,协同过滤是推荐系统中出现最早、应用最广泛的一种推荐算法。 针对传统协同过滤推荐算法存在的项目热门度偏差问题,提出了一种融合项目热门惩罚因子改进协同过滤推荐方法。 引入热门阈值,根据项目热门度将项目进行二分类,即热门项目( 项目热门度较高的项目) 和非热门项目( 项目热门度较低的项目) 。 重点针对热门项目,融合项目热门惩罚因子改进协同过滤推荐方法,降低热门项目的贡献,从而提升推荐精度。 在 MovieLens 100 K 数据集上对所提推荐方法进行实验验证。 实验结果表明,在参数取最优值时,所提推荐方法较为有效地降低了评分预测的平均绝对误差和均方根误差,一定程度上验证了项目热门惩罚因子的有效性。
Abstract:
Recommendation system is an important tool to solve the problem of information overload in the era of big data. Collaborativefiltering is the earliest and most widely used recommendation algorithm in recommendation system. Aiming at the problem of itempopularity bias in traditional collaborative filtering recommendation algorithm, an improved collaborative filtering recommendation methodintegrating item popularity punishment factor is proposed. The popularity threshold is introduced,and then items are classified into twocategories according to the item popularity ( items with high popularity) ,namely popular items and non-popular items ( items with low popularity) . Focusing on popular items,the collaborative filtering recommendation method is improved by integrating the item popularitypunishment factor to reduce the influence of popular items on neighbors,so as to improve the recommendation accuracy. The proposedmethod is experimentally verified on MovieLens 100K dataset. The experimental results show that the proposed recommendation methodcan effectively reduce the mean absolute error and root mean squared error of rating prediction when the parameters are taken the optimalvalue,which verifies the validity of the item popularity punishment to some extent.

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更新日期/Last Update: 2023-03-10